This workflow follows the Chainretrievalqa → Retrievervectorstore recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"nodes": [
{
"id": "49086cdf-a38c-4cb8-9be9-d3e6ea5bdde5",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1740,
1040
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "f0670721-92f4-422a-99c9-f9c2aa6fe21f",
"name": "Token Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
2380,
540
],
"parameters": {
"chunkSize": 500
},
"typeVersion": 1
},
{
"id": "fe80ecac-4f79-4b07-ad8e-60ab5f980cba",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1180,
-200
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "81b79248-08e8-4214-872b-1796e51ad0a4",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
744,
495
],
"parameters": {
"options": {}
},
"typeVersion": 1.3
},
{
"id": "e78f7b63-baef-4834-8f1b-aecfa9102d6c",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
844,
715
],
"parameters": {},
"typeVersion": 1
},
{
"id": "1d5ffbd0-b2cf-4660-a291-581d18608ecd",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
704,
715
],
"parameters": {
"model": "gpt-4o",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "37a3063f-aa21-4347-a72f-6dd316c58366",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
524,
495
],
"parameters": {
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "5924bc01-1694-4b5c-8a06-7c46ee4c6425",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
520,
-200
],
"parameters": {
"rule": {
"interval": [
{
"field": "minutes",
"minutesInterval": 1
}
]
}
},
"typeVersion": 1.2
},
{
"id": "5067eda6-8bbe-407a-a6af-93e81be53661",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
620,
0
],
"parameters": {
"width": 329.16412916774584,
"height": 312.52803480051045,
"content": "## Switch trigger (optional)\nIf you are on the cloud plan, consider switching to the Notion Trigger Node instead, to save on executions."
},
"typeVersion": 1
},
{
"id": "33458828-484d-426b-a3d1-974a81c6162e",
"name": "Limit",
"type": "n8n-nodes-base.limit",
"position": [
1620,
-60
],
"parameters": {},
"typeVersion": 1
},
{
"id": "4d39503a-378e-4942-a5d4-8c62785aac44",
"name": "Limit1",
"type": "n8n-nodes-base.limit",
"position": [
2660,
-60
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0e0b1391-3fe5-4d80-a2eb-a2483b79d9a6",
"name": "Delete old embeddings if exist",
"type": "n8n-nodes-base.supabase",
"position": [
1400,
-60
],
"parameters": {
"tableId": "documents",
"operation": "delete",
"filterType": "string",
"filterString": "=metadata->>id=eq.{{ $('Input Reference').item.json.id }}"
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "4a8614e4-0a53-4731-bc68-57505d7d0a09",
"name": "Get page blocks",
"type": "n8n-nodes-base.notion",
"position": [
1840,
-60
],
"parameters": {
"blockId": {
"__rl": true,
"mode": "id",
"value": "={{ $('Input Reference').item.json.id }}"
},
"resource": "block",
"operation": "getAll",
"returnAll": true,
"fetchNestedBlocks": true
},
"credentials": {
"notionApi": {
"name": "<your credential>"
}
},
"executeOnce": true,
"typeVersion": 2.2
},
{
"id": "8c922895-49d6-4778-8356-6f6cf49e5420",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
2300,
260
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "id",
"value": "={{ $('Input Reference').item.json.id }}"
},
{
"name": "name",
"value": "={{ $('Input Reference').item.json.name }}"
}
]
}
}
},
"typeVersion": 1
},
{
"id": "8ad7ff2e-4bc2-4821-ae03-bab2dc11d947",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
400
],
"parameters": {
"width": 376.2098538932132,
"height": 264.37628764336097,
"content": "## Adjust chunk size and overlap\nFor more accurate search results, increase the overlap. For the *text-embedding-ada-002* model the chunk size plus overlap must not exceed 8191"
},
"typeVersion": 1
},
{
"id": "8078d59a-f45f-4e96-a8ec-6c2f1c328e84",
"name": "Input Reference",
"type": "n8n-nodes-base.noOp",
"position": [
960,
-200
],
"parameters": {},
"typeVersion": 1
},
{
"id": "aae6c517-a316-40e3-aee9-1cc4b448689f",
"name": "Notion Trigger",
"type": "n8n-nodes-base.notionTrigger",
"disabled": true,
"position": [
740,
120
],
"parameters": {
"event": "pagedUpdatedInDatabase",
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"databaseId": {
"__rl": true,
"mode": "list",
"value": "ec6dc7b4-9ce0-47f7-8025-ef09295999fd",
"cachedResultUrl": "https://www.notion.so/ec6dc7b49ce047f78025ef09295999fd",
"cachedResultName": "Knowledge Base"
}
},
"credentials": {
"notionApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "3a43d66d-d4e3-4ca1-aee9-85ac65160e45",
"name": "Get updated pages",
"type": "n8n-nodes-base.notion",
"position": [
740,
-200
],
"parameters": {
"filters": {
"conditions": [
{
"key": "Last edited time|last_edited_time",
"condition": "equals",
"lastEditedTime": "={{ $now.minus(1, 'minutes').toISO() }}"
}
]
},
"options": {},
"resource": "databasePage",
"operation": "getAll",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "ec6dc7b4-9ce0-47f7-8025-ef09295999fd",
"cachedResultUrl": "https://www.notion.so/ec6dc7b49ce047f78025ef09295999fd",
"cachedResultName": "Knowledge Base"
},
"filterType": "manual"
},
"credentials": {
"notionApi": {
"name": "<your credential>"
}
},
"typeVersion": 2.2
},
{
"id": "bbf1296f-4e2b-4a38-bdf3-ae2b63cc7774",
"name": "Sticky Note23",
"type": "n8n-nodes-base.stickyNote",
"position": [
900,
-300
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "This placeholder serves as a reference point so it is easier to swap the data source with a different service"
},
"typeVersion": 1
},
{
"id": "631e1e10-0b52-4a17-89a4-769ac563321f",
"name": "Sticky Note24",
"type": "n8n-nodes-base.stickyNote",
"position": [
1340,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "All chunks of a previous version of the document are being deleted by filtering the meta data by the given ID"
},
"typeVersion": 1
},
{
"id": "6c830c83-4b70-4719-8e2a-26846e60085c",
"name": "Sticky Note25",
"type": "n8n-nodes-base.stickyNote",
"position": [
1560,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Reduce the active streams/items to just 1 to prevent the following nodes from double-processing"
},
"typeVersion": 1
},
{
"id": "46c8e4e4-0a5e-4ede-947b-5773710d4e55",
"name": "Sticky Note26",
"type": "n8n-nodes-base.stickyNote",
"position": [
1780,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Retrieve all page contents/blocks"
},
"typeVersion": 1
},
{
"id": "0369e610-d074-4812-9d04-8615b42965a5",
"name": "Sticky Note27",
"type": "n8n-nodes-base.stickyNote",
"position": [
2600,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Reduce the active streams/items to just 1 to prevent the following nodes from double-processing"
},
"typeVersion": 1
},
{
"id": "4f3bce54-1650-45fa-abb0-c881358c7e8d",
"name": "Sticky Note28",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
-160
],
"parameters": {
"color": 7,
"width": 375.9283286479995,
"height": 275.841854198618,
"content": "Embed item and store in Vector Store. Depending on the length the content is being split up into multiple chunks/embeds"
},
"typeVersion": 1
},
{
"id": "44125921-e068-4a5d-a56b-b0e63c103556",
"name": "Supabase Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
924,
935
],
"parameters": {
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "467322a9-949d-4569-aac6-92196da46ba5",
"name": "Sticky Note30",
"type": "n8n-nodes-base.stickyNote",
"position": [
460,
400
],
"parameters": {
"color": 7,
"width": 730.7522093855692,
"height": 668.724737081502,
"content": "Simple chat bot to ask specific questions while having access to the context of the Notion Knowledge Base which was stored in the Vector Store"
},
"typeVersion": 1
},
{
"id": "27f078cf-b309-4dd1-a8ce-b4fc504d6e29",
"name": "Sticky Note31",
"type": "n8n-nodes-base.stickyNote",
"position": [
1660,
900
],
"parameters": {
"color": 7,
"width": 219.31927574471658,
"height": 275.841854198618,
"content": "Model used for both creating and reading embeddings"
},
"typeVersion": 1
},
{
"id": "2f59cba1-4318-47e7-bf0b-b908d4186b86",
"name": "Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
2280,
-60
],
"parameters": {
"mode": "insert",
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "729849e7-0eff-40c2-ae00-ae660c1eec69",
"name": "Sticky Note32",
"type": "n8n-nodes-base.stickyNote",
"position": [
1120,
-300
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Process each page/document separately."
},
"typeVersion": 1
},
{
"id": "3f632a24-ca0a-45c4-801d-041aa3f887a7",
"name": "Sticky Note29",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
120
],
"parameters": {
"color": 7,
"width": 376.0759088111347,
"height": 275.841854198618,
"content": "Store additional meta data with each embed, especially the Notion ID, which can be later used to find all belonging entries of one page, even if they got split into multiple embeds."
},
"typeVersion": 1
},
{
"id": "ffaf3861-5287-4f57-8372-09216a18cb4d",
"name": "Sticky Note33",
"type": "n8n-nodes-base.stickyNote",
"position": [
460,
-300
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Using a manual approach for polling data from Notion for more accuracy."
},
"typeVersion": 1
},
{
"id": "cbbedfc0-4d64-42a6-8f55-21e04887305f",
"name": "Sticky Note34",
"type": "n8n-nodes-base.stickyNote",
"position": [
680,
-300
],
"parameters": {
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "## Select Database\nChoose the database which represents your Knowledge Base"
},
"typeVersion": 1
},
{
"id": "8b6767f2-1bc9-42fb-b319-f39f6734b9f2",
"name": "Sticky Note35",
"type": "n8n-nodes-base.stickyNote",
"position": [
2000,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Combine all contents to a single text formatted into one line which can be easily stored as an embed"
},
"typeVersion": 1
},
{
"id": "cdff1756-77d7-421e-8672-25c9862840b0",
"name": "Concatenate to single string",
"type": "n8n-nodes-base.summarize",
"position": [
2060,
-60
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "content",
"separateBy": "\n",
"aggregation": "concatenate"
}
]
}
},
"typeVersion": 1
}
],
"connections": {
"Limit": {
"main": [
[
{
"node": "Get page blocks",
"type": "main",
"index": 0
}
]
]
},
"Limit1": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Notion Trigger": {
"main": [
[
{
"node": "Input Reference",
"type": "main",
"index": 0
}
]
]
},
"Token Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Get page blocks": {
"main": [
[
{
"node": "Concatenate to single string",
"type": "main",
"index": 0
}
]
]
},
"Input Reference": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Delete old embeddings if exist",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Get updated pages",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store",
"type": "ai_embedding",
"index": 0
},
{
"node": "Supabase Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Get updated pages": {
"main": [
[
{
"node": "Input Reference",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Question and Answer Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Supabase Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Supabase Vector Store": {
"main": [
[
{
"node": "Limit1",
"type": "main",
"index": 0
}
]
]
},
"Supabase Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Question and Answer Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"Concatenate to single string": {
"main": [
[
{
"node": "Supabase Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Delete old embeddings if exist": {
"main": [
[
{
"node": "Limit",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
notionApiopenAiApisupabaseApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
This workflow enables you to effortlessly upload and query vast documents stored in a Supabase vector database, powered by Notion for seamless content management, transforming complex information retrieval into a simple chat-based experience. It's ideal for researchers, knowledge workers, or teams handling extensive reports, articles, or databases who need quick, accurate answers without manual searching. The key step involves splitting large documents into manageable chunks using a token splitter, generating embeddings with OpenAI, and upserting them into the vector store for intelligent retrieval and question-answering.
Use this workflow when dealing with documents exceeding typical size limits that require semantic search, such as legal archives or technical manuals integrated via Notion. Avoid it for small, static files better suited to basic databases, or when real-time collaboration isn't needed. Common variations include swapping OpenAI for alternative embedding models or adding post-retrieval summarisation for concise responses.
About this workflow
Upsert Huge Documents In A Vector Store With Supabase And Notion. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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